Dataset: Covid-19 Food Insecurity Data

About this Dataset

Covid-19 Food Insecurity Data

What's in the Data?

This dataset tracks food insecurity across different demographics starting 4/23/2020 to 8/23/2021. It contains fields such as Race, Education, Sex, State, Income, etc. If you're looking for a dataset to examine Covid-19's impact on food insecurity for different demographics, then here you are!

Data Source

This data is from the United States Census Bureau's Pulse Survey. The Pulse Survey is a frequently updating survey designed to collect data on how people's lives have been impacted by the coronavirus. Specifically, this dataset is a cleaned up version of the ' Food Sufficiency for Households, in the Last 7 Days, by Select Characteristics" tables.

The original form of this data can be found at: https://www.census.gov/programs-surveys/household-pulse-survey/data.html

What was done to this data?

The original form of this data was split into 36 excel files containing ~67 sheets each. The data was in a non-tidy format, and questions were also not entirely standard. This dataset is my attempt to combine all these different files, tidy the data up, and combine slightly different questions together.

Why are there so many NA's?

The large amount of NA's are a consequence of how awful the data was originally/ forcing the data into a tidy format. Just filter the NA's out for the question you want to analyze and you'll be fine.

Tables

Final Pulse Data

@kaggle.jackogozaly_pulse_survey_food_insecurity_data.final_pulse_data

7.63 MB
305788 rows
40 columns


CREATE TABLE final_pulse_data (
  "enough_of_the_kinds_of_food_wanted" DOUBLE,
  "enough_food_but_not_always_the_kinds_wanted" DOUBLE,
  "sometimes_not_enough_to_eat" DOUBLE,
  "often_not_enough_to_eat" DOUBLE,
  "did_not_report" DOUBLE,
  "age" VARCHAR,
  "sex_at_birth" VARCHAR,
  "race" VARCHAR,
  "education" VARCHAR,
  "marital_status" VARCHAR,
  "children_present" VARCHAR,
  "unemployed_last_4" VARCHAR,
  "employed_last_7" VARCHAR,
  "reason_not_working" VARCHAR,
  "receiving_pay_no_work" VARCHAR,
  "income" VARCHAR,
  "health_status" VARCHAR,
  "freq_feel_anxious" VARCHAR,
  "freq_feel_control_worry" VARCHAR,
  "freq_feel_lil_interest" VARCHAR,
  "freq_feel_depressed" VARCHAR,
  "covered_by_insurance" VARCHAR,
  "caught_up_on_mortgage" VARCHAR,
  "conf_pay_next_rent_or_mortgage" VARCHAR,
  "free_groceries" VARCHAR,
  "provider_of_free_groceries" VARCHAR,
  "location" VARCHAR,
  "week" VARCHAR,
  "household_size" VARCHAR,
  "used_to_meet_needs" VARCHAR,
  "house_receives_snap" VARCHAR,
  "active_duty" VARCHAR,
  "difficulty_seeing" VARCHAR,
  "difficulty_hearing" VARCHAR,
  "difficulty_remembering" VARCHAR,
  "difficulty_walking_climbing" VARCHAR,
  "sexual_orientation" VARCHAR,
  "lgbt" VARCHAR,
  "week_name" VARCHAR,
  "year" BIGINT
);